Portfolio Tail Risk: An E fficient Multivariate Extreme Value Theory Approach

2013 ◽  
Author(s):  
Milos Bozovic
2019 ◽  
Vol 35 (2) ◽  
pp. 607-628
Author(s):  
Maël Chiapino ◽  
Stephan Clémençon ◽  
Vincent Feuillard ◽  
Anne Sabourin

2014 ◽  
Vol 2 (1) ◽  
Author(s):  
Anne Dutfoy ◽  
Sylvie Parey ◽  
Nicolas Roche

AbstractIn this paper, we provide a tutorial on multivariate extreme value methods which allows to estimate the risk associated with rare events occurring jointly. We draw particular attention to issues related to extremal dependence and we insist on the asymptotic independence feature. We apply the multivariate extreme value theory on two data sets related to hydrology and meteorology: first, the joint flooding of two rivers, which puts at risk the facilities lying downstream the confluence; then the joint occurrence of high speed wind and low air temperatures, which might affect overhead lines.


Author(s):  
Gianfausto Salvadori ◽  
Carlo De Michele ◽  
Nathabandu T. Kottegoda ◽  
Renzo Rosso

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1425
Author(s):  
Miloš Božović

This paper develops a method for assessing portfolio tail risk based on extreme value theory. The technique applies separate estimations of univariate series and allows for closed-form expressions for Value at Risk and Expected Shortfall. Its forecasting ability is tested on a portfolio of U.S. stocks. The in-sample goodness-of-fit tests indicate that the proposed approach is better suited for portfolio risk modeling under extreme market movements than comparable multivariate parametric methods. Backtesting across multiple quantiles demonstrates that the model cannot be rejected at any reasonable level of significance, even when periods of stress are included. Numerical simulations corroborate the empirical results.


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